def main(argv):
parser = argparse.ArgumentParser(
description='Compute codes and reconstructions for image.')
parser.add_argument('export_dir', type=str, help='Path to saved model.')
parser.add_argument(
'mean',
type=str,
help='Path to npy file holding mean for normalization.')
parser.add_argument(
'variance',
type=str,
help='Path to npy file holding variance for normalization.')
parser.add_argument('source_filename',
type=str,
help='Image file from which to extract patch.')
parser.add_argument('source_image_size',
type=int,
nargs=2,
help='Size of the input image, HW.')
parser.add_argument('offsets',
type=int,
nargs=2,
help='Position where to extract the patch.')
parser.add_argument('patch_size', type=int, help='Size of image patch.')
parser.add_argument('target_filename',
type=str,
help='Image file for which to create the heatmap.')
parser.add_argument(
'target_image_size',
type=int,
nargs=2,
help='Size of the input image for which to create heatmap, HW.')
parser.add_argument(
'method',
type=str,
help=
'Method to use to measure similarity, one of KLD, SKLD, BD, HD, SQHD.')
parser.add_argument(
'--stain_code_size',
type=int,
dest='stain_code_size',
default=0,
help=
'Optional: Size of the stain code to use, which is skipped for similarity estimation'
)
parser.add_argument('--rotate',
type=float,
dest='angle',
default=0,
help='Optional: rotation angle to rotate target image')
parser.add_argument('--subsampling_factor',
type=int,
dest='subsampling_factor',
default=1,
help='Factor to subsample source and target image.')
args = parser.parse_args()
mean = np.load(args.mean)
variance = np.load(args.variance)
stddev = [np.math.sqrt(x) for x in variance]
def denormalize(image):
channels = [
np.expand_dims(
image[:, :, channel] * stddev[channel] + mean[channel], -1)
for channel in range(3)
]
denormalized_image = ctfi.rescale(np.concatenate(channels, 2), 0.0,
1.0)
return denormalized_image
def normalize(image, name=None):
channels = [
tf.expand_dims(
(image[:, :, :, channel] - mean[channel]) / stddev[channel],
-1) for channel in range(3)
]
return tf.concat(channels, 3, name=name)
latest_checkpoint = tf.train.latest_checkpoint(args.export_dir)
saver = tf.train.import_meta_graph(latest_checkpoint + '.meta',
import_scope='imported')
with tf.Session(graph=tf.get_default_graph()).as_default() as sess:
# Load image and extract patch from it and create distribution.
source_image = ctfi.subsample(
ctfi.load(args.source_filename,
height=args.source_image_size[0],
width=args.source_image_size[1]),
args.subsampling_factor)
args.source_image_size = list(
map(lambda x: int(x / args.subsampling_factor),
args.source_image_size))
patch = normalize(
tf.expand_dims(
tf.image.crop_to_bounding_box(source_image, args.offsets[0],
args.offsets[1], args.patch_size,
args.patch_size), 0))
#patch_cov, patch_mean = tf.contrib.graph_editor.graph_replace([sess.graph.get_tensor_by_name('imported/z_covariance_lower_tri/MatrixBandPart:0'),sess.graph.get_tensor_by_name('imported/z_mean/BiasAdd:0')] ,{ sess.graph.get_tensor_by_name('imported/patch:0'): patch })
#patch_distribution = tf.contrib.distributions.MultivariateNormalTriL(loc=patch_mean[:,args.stain_code_size:], scale_tril=patch_cov[:,args.stain_code_size:,args.stain_code_size:])
patch_cov, patch_mean = tf.contrib.graph_editor.graph_replace([
sess.graph.get_tensor_by_name('imported/z_log_sigma_sq/BiasAdd:0'),
sess.graph.get_tensor_by_name('imported/z_mean/BiasAdd:0')
], {sess.graph.get_tensor_by_name('imported/patch:0'):
patch})
patch_distribution = tf.contrib.distributions.MultivariateNormalDiag(
patch_mean[:, args.stain_code_size:],
tf.sqrt(tf.exp(patch_cov[:, args.stain_code_size:])))
sim_vals = []
#Load image for which to create the heatmap
target_image = ctfi.subsample(
ctfi.load(args.target_filename,
height=args.target_image_size[0],
width=args.target_image_size[1]),
args.subsampling_factor)
target_image = tf.contrib.image.rotate(target_image,
np.radians(args.angle))
args.target_image_size = list(
map(lambda x: int(x / args.subsampling_factor),
args.target_image_size))
heatmap_height = args.target_image_size[0] - (args.patch_size - 1)
heatmap_width = args.target_image_size[1] - (args.patch_size - 1)
# Compute byte size as: width*height*channels*sizeof(float32)
patch_size_in_byte = args.patch_size**2 * 3 * 4
max_patches = int(max_patch_buffer_size / patch_size_in_byte)
max_num_rows = int(max_patches / heatmap_width)
max_chunk_size = int(max_buffer_size_in_byte / patch_size_in_byte)
#Iteration over image regions that we can load
num_iterations = int(args.target_image_size[0] / max_num_rows) + 1
all_chunks = list()
all_similarities = list()
chunk_tensors = list()
chunk_sizes = np.zeros(num_iterations, dtype=np.int)
chunk_sizes.fill(heatmap_width)
for i in range(num_iterations):
processed_rows = i * max_num_rows
rows_to_load = min(max_num_rows + (args.patch_size - 1),
args.target_image_size[0] - processed_rows)
if rows_to_load < args.patch_size:
break
# Extract region for which we can compute patches
target_image_region = tf.image.crop_to_bounding_box(
target_image, processed_rows, 0, rows_to_load,
args.target_image_size[1])
# Size = (image_width - patch_size - 1) * (image_height - patch_size - 1) for 'VALID' padding and
# image_width * image_height for 'SAME' padding
all_image_patches = tf.unstack(
normalize(
ctfi.extract_patches(target_image_region,
args.patch_size,
strides=[1, 1, 1, 1],
padding='VALID')))
possible_chunk_sizes = get_divisors(len(all_image_patches))
for size in possible_chunk_sizes:
if size < max_chunk_size:
chunk_sizes[i] = size
break
# Partition patches into chunks
chunked_patches = list(
create_chunks(all_image_patches, chunk_sizes[i]))
chunked_patches = list(map(tf.stack, chunked_patches))
all_chunks.append(chunked_patches)
#last_chunk = chunked_patches.pop()
#last_chunk_size = last_chunk.get_shape().as_list()[0]
#padding_size = chunk_size - last_chunk_size
#last_chunk_padded = tf.concat([last_chunk, tf.ones([padding_size, args.patch_size, args.patch_size, 3],dtype=tf.float32)],0)
chunk_tensor = tf.placeholder(
tf.float32,
shape=[chunk_sizes[i], args.patch_size, args.patch_size, 3],
name='chunk_tensor_placeholder')
chunk_tensors.append(chunk_tensor)
#image_patches_cov, image_patches_mean = tf.contrib.graph_editor.graph_replace([sess.graph.get_tensor_by_name('imported/z_covariance_lower_tri/MatrixBandPart:0'),sess.graph.get_tensor_by_name('imported/z_mean/BiasAdd:0')] ,{ sess.graph.get_tensor_by_name('imported/patch:0'): chunk_tensor })
#image_patches_distributions = tf.contrib.distributions.MultivariateNormalTriL(loc=image_patches_mean[:,args.stain_code_size:], scale_tril=image_patches_cov[:,args.stain_code_size:,args.stain_code_size:])
image_patches_cov, image_patches_mean = tf.contrib.graph_editor.graph_replace(
[
sess.graph.get_tensor_by_name(
'imported/z_log_sigma_sq/BiasAdd:0'),
sess.graph.get_tensor_by_name('imported/z_mean/BiasAdd:0')
], {
sess.graph.get_tensor_by_name('imported/patch:0'):
chunk_tensor
})
image_patches_distributions = tf.contrib.distributions.MultivariateNormalDiag(
image_patches_mean[:, args.stain_code_size:],
tf.sqrt(tf.exp(image_patches_cov[:, args.stain_code_size:])))
if args.method == 'SKLD':
similarities = patch_distribution.kl_divergence(
image_patches_distributions
) + image_patches_distributions.kl_divergence(
patch_distribution)
elif args.method == 'BD':
similarities = ctf.bhattacharyya_distance(
patch_distribution, image_patches_distributions)
elif args.method == 'SQHD':
similarities = ctf.multivariate_squared_hellinger_distance(
patch_distribution, image_patches_distributions)
elif args.method == 'HD':
similarities = tf.sqrt(
ctf.multivariate_squared_hellinger_distance(
patch_distribution, image_patches_distributions))
else:
similarities = patch_distribution.kl_divergence(
image_patches_distributions)
all_similarities.append(similarities)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
saver.restore(sess, latest_checkpoint)
for i in range(len(all_chunks)):
for chunk in all_chunks[i]:
#chunk_vals = sess.run(all_similarities[i], feed_dict={chunk_tensors[i]: sess.run(chunk)})
sim_vals.extend(
sess.run(all_similarities[i],
feed_dict={chunk_tensors[i]: sess.run(chunk)}))
print(len(sim_vals))
sim_heatmap = np.reshape(sim_vals, [heatmap_height, heatmap_width])
heatmap_tensor = tf.expand_dims(
tf.expand_dims(tf.convert_to_tensor(sim_heatmap), -1), 0)
dy, dx = tf.image.image_gradients(heatmap_tensor)
sim_vals_normalized = 1.0 - ctfi.rescale(sim_heatmap, 0.0, 1.0)
k_min = 20
min_indices = np.unravel_index(
np.argsort(sim_vals)[:k_min], sim_heatmap.shape)
fig_min, ax_min = plt.subplots(4, 5)
for i in range(k_min):
target_patch = tf.image.crop_to_bounding_box(
target_image, min_indices[0][i], min_indices[1][i],
args.patch_size, args.patch_size)
ax_min[int(i / 5), int(i % 5)].imshow(sess.run(target_patch))
ax_min[int(i / 5),
int(i % 5)].set_title('y:' + str(min_indices[0][i]) +
', x:' + str(min_indices[1][i]))
fig, ax = plt.subplots(2, 3)
cmap = 'plasma'
denormalized_patch = denormalize(sess.run(patch)[0])
max_sim_val = np.max(sim_vals)
max_idx = np.unravel_index(np.argmin(sim_heatmap), sim_heatmap.shape)
target_image_patch = tf.image.crop_to_bounding_box(
target_image, max_idx[0], max_idx[1], args.patch_size,
args.patch_size)
print(max_idx)
print(min_indices)
ax[1, 0].imshow(sess.run(source_image))
ax[1, 1].imshow(sess.run(target_image))
ax[0, 0].imshow(denormalized_patch)
heatmap_image = ax[0, 2].imshow(sim_heatmap, cmap=cmap)
ax[0, 1].imshow(sess.run(target_image_patch))
#dx_image = ax[0,2].imshow(np.squeeze(sess.run(dx)), cmap='bwr')
#dy_image = ax[1,2].imshow(np.squeeze(sess.run(dy)), cmap='bwr')
gradient_image = ax[1, 2].imshow(np.squeeze(sess.run(dx + dy)),
cmap='bwr')
fig.colorbar(heatmap_image, ax=ax[0, 2])
#fig.colorbar(dx_image, ax=ax[0,2])
#fig.colorbar(dy_image, ax=ax[1,2])
fig.colorbar(gradient_image, ax=ax[1, 2])
plt.show()
sess.close()
print("Done!")
def main(argv):
parser = argparse.ArgumentParser(description='Compute similarity heatmaps of windows around landmarks.')
parser.add_argument('export_dir',type=str,help='Path to saved model.')
parser.add_argument('mean', type=str, help='Path to npy file holding mean for normalization.')
parser.add_argument('variance', type=str, help='Path to npy file holding variance for normalization.')
parser.add_argument('source_filename', type=str,help='Image file from which to extract patch.')
parser.add_argument('source_image_size', type=int, nargs=2, help='Size of the input image, HW.')
parser.add_argument('source_landmarks', type=str,help='CSV file from which to extract the landmarks for source image.')
parser.add_argument('target_filename', type=str,help='Image file for which to create the heatmap.')
parser.add_argument('target_image_size', type=int, nargs=2, help='Size of the input image for which to create heatmap, HW.')
parser.add_argument('target_landmarks', type=str,help='CSV file from which to extract the landmarks for target image.')
parser.add_argument('patch_size', type=int, help='Size of image patch.')
parser.add_argument('output', type=str)
parser.add_argument('--method', dest='method', type=str, help='Method to use to measure similarity, one of KLD, SKLD, BD, HD, SQHD.')
parser.add_argument('--stain_code_size', type=int, dest='stain_code_size', default=0,
help='Optional: Size of the stain code to use, which is skipped for similarity estimation')
parser.add_argument('--rotate', type=float, dest='angle', default=0,
help='Optional: rotation angle to rotate target image')
parser.add_argument('--subsampling_factor', type=int, dest='subsampling_factor', default=1, help='Factor to subsample source and target image.')
parser.add_argument('--region_size', type=int, default=64)
args = parser.parse_args()
mean = np.load(args.mean)
variance = np.load(args.variance)
stddev = [np.math.sqrt(x) for x in variance]
def denormalize(image):
channels = [np.expand_dims(image[:,:,channel] * stddev[channel] + mean[channel],-1) for channel in range(3)]
denormalized_image = ctfi.rescale(np.concatenate(channels, 2), 0.0, 1.0)
return denormalized_image
def normalize(image, name=None, num_channels=3):
channels = [tf.expand_dims((image[:,:,:,channel] - mean[channel]) / stddev[channel],-1) for channel in range(num_channels)]
return tf.concat(channels, num_channels)
latest_checkpoint = tf.train.latest_checkpoint(args.export_dir)
saver = tf.train.import_meta_graph(latest_checkpoint + '.meta', import_scope='imported')
config = tf.ConfigProto()
config.allow_soft_placement=True
#config.log_device_placement=True
# Load image and extract patch from it and create distribution.
source_image = tf.expand_dims(ctfi.subsample(ctfi.load(args.source_filename,height=args.source_image_size[0], width=args.source_image_size[1]),args.subsampling_factor),0)
args.source_image_size = list(map(lambda x: int(x / args.subsampling_factor), args.source_image_size))
#Load image for which to create the heatmap
target_image = tf.expand_dims(ctfi.subsample(ctfi.load(args.target_filename,height=args.target_image_size[0], width=args.target_image_size[1]),args.subsampling_factor),0)
args.target_image_size = list(map(lambda x: int(x / args.subsampling_factor), args.target_image_size))
source_landmarks = get_landmarks(args.source_landmarks, args.subsampling_factor)
target_landmarks = get_landmarks(args.target_landmarks, args.subsampling_factor)
region_size = args.region_size
region_center = [int(region_size / 2),int(region_size / 2)]
num_patches = region_size**2
possible_splits = cutil.get_divisors(num_patches)
num_splits = possible_splits.pop(0)
while num_patches / num_splits > 512 and len(possible_splits) > 0:
num_splits = possible_splits.pop(0)
split_size = int(num_patches / num_splits)
offset = 64
center_idx = np.prod(region_center)
X, Y = np.meshgrid(range(offset, region_size + offset), range(offset, region_size + offset))
coords = np.concatenate([np.expand_dims(Y.flatten(),axis=1),np.expand_dims(X.flatten(),axis=1)],axis=1)
coords_placeholder = tf.placeholder(tf.float32, shape=[split_size, 2])
source_landmark_placeholder = tf.placeholder(tf.float32, shape=[1, 2])
target_landmark_placeholder = tf.placeholder(tf.float32, shape=[1, 2])
source_image_region = tf.image.extract_glimpse(source_image,[region_size + 2*offset, region_size+ 2*offset], source_landmark_placeholder, normalized=False, centered=False)
target_image_region = tf.image.extract_glimpse(target_image,[region_size + 2*offset, region_size+ 2*offset], target_landmark_placeholder, normalized=False, centered=False)
source_patches_placeholder = tf.map_fn(lambda x: get_patch_at(x, source_image, args.patch_size), source_landmark_placeholder, parallel_iterations=8, back_prop=False)[0]
target_patches_placeholder = tf.squeeze(tf.map_fn(lambda x: get_patch_at(x, target_image_region, args.patch_size), coords_placeholder, parallel_iterations=8, back_prop=False))
with tf.Session(config=config).as_default() as sess:
saver.restore(sess, latest_checkpoint)
source_patches_cov, source_patches_mean = tf.contrib.graph_editor.graph_replace([sess.graph.get_tensor_by_name('imported/z_log_sigma_sq/BiasAdd:0'),sess.graph.get_tensor_by_name('imported/z_mean/BiasAdd:0')] ,{ sess.graph.get_tensor_by_name('imported/patch:0'): normalize(source_patches_placeholder) })
source_patches_distribution = tf.contrib.distributions.MultivariateNormalDiag(source_patches_mean[:,args.stain_code_size:], tf.exp(source_patches_cov[:,args.stain_code_size:]))
target_patches_cov, target_patches_mean = tf.contrib.graph_editor.graph_replace([sess.graph.get_tensor_by_name('imported/z_log_sigma_sq/BiasAdd:0'),sess.graph.get_tensor_by_name('imported/z_mean/BiasAdd:0')] ,{ sess.graph.get_tensor_by_name('imported/patch:0'): normalize(target_patches_placeholder) })
target_patches_distribution = tf.contrib.distributions.MultivariateNormalDiag(target_patches_mean[:,args.stain_code_size:], tf.exp(target_patches_cov[:,args.stain_code_size:]))
similarities_skld = source_patches_distribution.kl_divergence(target_patches_distribution) + target_patches_distribution.kl_divergence(source_patches_distribution)
similarities_bd = ctf.bhattacharyya_distance(source_patches_distribution, target_patches_distribution)
similarities_sad = tf.reduce_sum(tf.abs(source_patches_placeholder - target_patches_placeholder), axis=[1,2,3])
source_patches_grayscale = tf.image.rgb_to_grayscale(source_patches_placeholder)
target_patches_grayscale = tf.image.rgb_to_grayscale(target_patches_placeholder)
similarities_nmi = tf.map_fn(lambda x: nmi_tf(tf.squeeze(source_patches_grayscale), tf.squeeze(x), 20), target_patches_grayscale)
with open(args.output + "_" + str(region_size) + ".csv",'wt') as outfile:
fp = csv.DictWriter(outfile, ["method", "landmark", "min_idx", "min_idx_value", "rank", "landmark_value"])
methods = ["SKLD", "BD", "SAD", "MI"]
fp.writeheader()
results = []
for k in range(len(source_landmarks)):
heatmap_fused = np.ndarray((region_size, region_size, len(methods)))
feed_dict={source_landmark_placeholder: [source_landmarks[k,:]], target_landmark_placeholder: [target_landmarks[k,:]] }
for i in range(num_splits):
start = i * split_size
end = start + split_size
batch_coords = coords[start:end,:]
feed_dict.update({coords_placeholder: batch_coords})
similarity_values = np.array(sess.run([similarities_skld,similarities_bd, similarities_sad, similarities_nmi],feed_dict=feed_dict)).transpose()
#heatmap.extend(similarity_values)
for idx, val in zip(batch_coords, similarity_values):
heatmap_fused[idx[0] - offset, idx[1] - offset] = val
for c in range(len(methods)):
heatmap = heatmap_fused[:,:,c]
if c == 3:
min_idx = np.unravel_index(np.argmax(heatmap),heatmap.shape)
min_indices = np.array(np.unravel_index(list(reversed(np.argsort(heatmap.flatten()))),heatmap.shape)).transpose().tolist()
else:
min_idx = np.unravel_index(np.argmin(heatmap),heatmap.shape)
min_indices = np.array(np.unravel_index(np.argsort(heatmap.flatten()),heatmap.shape)).transpose().tolist()
landmark_value = heatmap[region_center[0], region_center[1]]
rank = min_indices.index(region_center)
fp.writerow({"method": methods[c],"landmark": k, "min_idx": min_idx, "min_idx_value": heatmap[min_idx[0], min_idx[1]],"rank": rank , "landmark_value": landmark_value})
#matplotlib.image.imsave(args.output + "_" + str(region_size)+ "_"+ methods[c] + "_" + str(k) + ".jpeg", heatmap, cmap='plasma')
outfile.flush()
print(min_idx, rank)
fp.writerows(results)
sess.close()
return 0
def main(argv):
parser = argparse.ArgumentParser(
description='Compute codes and reconstructions for image.')
parser.add_argument('export_dir', type=str, help='Path to saved model.')
parser.add_argument(
'mean',
type=str,
help='Path to npy file holding mean for normalization.')
parser.add_argument(
'variance',
type=str,
help='Path to npy file holding variance for normalization.')
parser.add_argument('source_filename',
type=str,
help='Image file from which to extract patch.')
parser.add_argument('source_image_size',
type=int,
nargs=2,
help='Size of the input image, HW.')
parser.add_argument(
'source_landmarks',
type=str,
help='CSV file from which to extract the landmarks for source image.')
parser.add_argument('target_filename',
type=str,
help='Image file for which to create the heatmap.')
parser.add_argument(
'target_image_size',
type=int,
nargs=2,
help='Size of the input image for which to create heatmap, HW.')
parser.add_argument(
'target_landmarks',
type=str,
help='CSV file from which to extract the landmarks for target image.')
parser.add_argument('patch_size', type=int, help='Size of image patch.')
parser.add_argument(
'--method',
dest='method',
type=str,
help=
'Method to use to measure similarity, one of KLD, SKLD, BD, HD, SQHD.')
parser.add_argument(
'--stain_code_size',
type=int,
dest='stain_code_size',
default=0,
help=
'Optional: Size of the stain code to use, which is skipped for similarity estimation'
)
parser.add_argument('--rotate',
type=float,
dest='angle',
default=0,
help='Optional: rotation angle to rotate target image')
parser.add_argument('--subsampling_factor',
type=int,
dest='subsampling_factor',
default=1,
help='Factor to subsample source and target image.')
args = parser.parse_args()
mean = np.load(args.mean)
variance = np.load(args.variance)
stddev = [np.math.sqrt(x) for x in variance]
def denormalize(image):
channels = [
np.expand_dims(
image[:, :, channel] * stddev[channel] + mean[channel], -1)
for channel in range(3)
]
denormalized_image = ctfi.rescale(np.concatenate(channels, 2), 0.0,
1.0)
return denormalized_image
def normalize(image, name=None, num_channels=3):
channels = [
tf.expand_dims(
(image[:, :, :, channel] - mean[channel]) / stddev[channel],
-1) for channel in range(num_channels)
]
return tf.concat(channels, num_channels)
latest_checkpoint = tf.train.latest_checkpoint(args.export_dir)
saver = tf.train.import_meta_graph(latest_checkpoint + '.meta',
import_scope='imported')
config = tf.ConfigProto()
config.allow_soft_placement = True
#config.log_device_placement=True
# Load image and extract patch from it and create distribution.
source_image = tf.expand_dims(
ctfi.subsample(
ctfi.load(args.source_filename,
height=args.source_image_size[0],
width=args.source_image_size[1]),
args.subsampling_factor), 0)
args.source_image_size = list(
map(lambda x: int(x / args.subsampling_factor),
args.source_image_size))
#Load image for which to create the heatmap
target_image = tf.expand_dims(
ctfi.subsample(
ctfi.load(args.target_filename,
height=args.target_image_size[0],
width=args.target_image_size[1]),
args.subsampling_factor), 0)
args.target_image_size = list(
map(lambda x: int(x / args.subsampling_factor),
args.target_image_size))
source_landmarks = get_landmarks(args.source_landmarks,
args.subsampling_factor)
source_patches = tf.squeeze(
tf.map_fn(lambda x: get_patch_at(x, source_image, args.patch_size),
source_landmarks))
target_landmarks = get_landmarks(args.target_landmarks,
args.subsampling_factor)
target_patches = tf.squeeze(
tf.map_fn(lambda x: get_patch_at(x, target_image, args.patch_size),
target_landmarks))
with tf.Session(config=config).as_default() as sess:
saver.restore(sess, latest_checkpoint)
source_patches_cov, source_patches_mean = tf.contrib.graph_editor.graph_replace(
[
sess.graph.get_tensor_by_name(
'imported/z_log_sigma_sq/BiasAdd:0'),
sess.graph.get_tensor_by_name('imported/z_mean/BiasAdd:0')
], {
sess.graph.get_tensor_by_name('imported/patch:0'):
normalize(source_patches)
})
source_patches_distribution = tf.contrib.distributions.MultivariateNormalDiag(
source_patches_mean[:, args.stain_code_size:],
tf.exp(source_patches_cov[:, args.stain_code_size:]))
target_patches_cov, target_patches_mean = tf.contrib.graph_editor.graph_replace(
[
sess.graph.get_tensor_by_name(
'imported/z_log_sigma_sq/BiasAdd:0'),
sess.graph.get_tensor_by_name('imported/z_mean/BiasAdd:0')
], {
sess.graph.get_tensor_by_name('imported/patch:0'):
normalize(target_patches)
})
target_patches_distribution = tf.contrib.distributions.MultivariateNormalDiag(
target_patches_mean[:, args.stain_code_size:],
tf.exp(target_patches_cov[:, args.stain_code_size:]))
#similarities = source_patches_distribution.kl_divergence(target_patches_distribution) + target_patches_distribution.kl_divergence(source_patches_distribution)
#similarities = ctf.multivariate_squared_hellinger_distance(source_patches_distribution, target_patches_distribution)
similarities = ctf.bhattacharyya_distance(source_patches_distribution,
target_patches_distribution)
#similarities = tf.reduce_sum(tf.abs(source_patches - target_patches), axis=[1,2,3])
sim_vals = sess.run(similarities)
min_idx = np.argmin(sim_vals)
max_idx = np.argmax(sim_vals)
print(sim_vals)
print(min_idx, sim_vals[min_idx])
print(max_idx, sim_vals[max_idx])
fig, ax = plt.subplots(2, 3)
ax[0, 0].imshow(sess.run(source_image[0]))
ax[0, 1].imshow(sess.run(source_patches)[min_idx])
ax[0, 2].imshow(sess.run(source_patches)[max_idx])
ax[1, 0].imshow(sess.run(target_image[0]))
ax[1, 1].imshow(sess.run(target_patches)[min_idx])
ax[1, 2].imshow(sess.run(target_patches)[max_idx])
plt.show()
sess.close()
return 0